def resolve_test_case(model, data_path, label_path, idp_complete_classes, rD_min, rD_max, rA_min, rA_max, is_plot=True):
special_replacement = {'Act': '0', 'Spc': '1', 'Bspc': '2', 'Ent': '3'}
data = pickle.load(open(data_path, 'rb'))
label = pickle.load(open(label_path, 'rb'))
sequence = np.reshape(np.array(label), newshape=(-1, 1))
sequence = np.array([[x[0]] for x in sequence if x[0] in idp_complete_classes])
valid_indices = np.argmax(encoder.transform(sequence).toarray(), axis=1)
index_class_dict = dict([(index, clss[0]) for index, clss in zip(valid_indices, sequence)])
rD_seq = np.array(data['mmw']['range_doppler'])
rA_seq = np.array(data['mmw']['range_azi'])
rD_seq = (rD_seq - rD_min) / (rD_max - rD_min)
rA_seq = (rA_seq - rA_min) / (rA_max - rA_min)
rA_samples = prepare_x(rA_seq, window_size=120, stride=1)
rD_samples = prepare_x(rD_seq, window_size=120, stride=1)
print('predicting on samples')
y_pred = model.predict([rD_samples, rA_samples], batch_size=32)
if is_plot:
matplotlib.rcParams.update({'font.size': 14})
plt.figure(figsize=(20, 6))
is_plotted_others = False
for i, col in enumerate(np.transpose(y_pred)):
if i in index_class_dict.keys():
plt.plot(col, label='Predicted gesture: ' + index_class_dict[i], linewidth=3)
else:
plt.plot(col, c='gray', label='Gestures for other chars') if not is_plotted_others else plt.plot(col,
c='gray')
is_plotted_others = True
debouncer_result = debouncer_detection(y_pred, is_plot=is_plot)
pred_string = replace_special(''.join(debouncer_result), special_replacement)
grdt_string = replace_special(''.join([s[0] for s in sequence]), special_replacement)
dist = levenshtein_ratio_and_distance(pred_string, grdt_string, ratio_calc=True)
print('detected: ' + pred_string)
print('groundtr: ' + grdt_string)
print('Distance = ' + str(dist))
return dist
Ent_index + 1
]) # H, E, L, L, O, spc, W, O, R, L, D
ys = np.array([Y[ki] for ki in key_indices])
print('Working with sequence: ' + str(encoder.inverse_transform(ys)))
rA_seq = np.array([X_mmw_rA[i] for i in key_indices])
rA_seq = np.reshape(
rA_seq, newshape=[-1] + list(rA_seq.shape[2:])
) # flatten the sample dimension to create temporal sequence
rD_seq = np.array([X_mmw_rD[i] for i in key_indices])
rD_seq = np.reshape(
rD_seq, newshape=[-1] + list(rD_seq.shape[2:])
) # flatten the sample dimension to create temporal sequence
# sample from the temporal sequence
rA_samples = prepare_x(rA_seq, window_size=121, stride=1)
rD_samples = prepare_x(rD_seq, window_size=121, stride=1)
y_pred = idp_model.predict([rD_samples, rA_samples], batch_size=32)
y_pred = y_pred[60:len(y_pred) - 60]
# plottings
matplotlib.rcParams.update({'font.size': 14})
plt.figure(figsize=(20, 6))
is_plotted_others = False
for i, col in enumerate(np.transpose(y_pred)):
if i in valid_indices:
plt.plot(moving_average(col, n=16),
label='Predicted gesture: ' + index_class_dict[i],
linewidth=3)